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                <div class="container"><article class="page"><h1 class="post-title animated flipInX">2021美赛</h1><div class="post-meta">
            <div class="post-meta-main"><a class="author" href="/" rel="author" target="_blank">
                    <i class="fas fa-user-circle fa-fw"></i>ChenDong Zhu
                </a>&nbsp;<span class="post-category">收录于&nbsp;<i class="far fa-folder fa-fw"></i><a href="/categories/%E6%AF%94%E8%B5%9B/">比赛</a>&nbsp;</span></div>
            <div class="post-meta-other"><i class="far fa-calendar-alt fa-fw"></i><time datetime=2021-02-09>2021-02-09</time>&nbsp;
                <i class="fas fa-pencil-alt fa-fw"></i>约 1369 字&nbsp;
                <i class="far fa-clock fa-fw"></i>预计阅读 3 分钟&nbsp;</div>
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                <h2 class="post-toc-title">目录</h2>
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  <ul>
    <li><a href="#2021-美赛">2021-美赛</a>
      <ul>
        <li><a href="#day-1">Day 1</a></li>
        <li><a href="#day-2">Day 2</a></li>
        <li><a href="#day-3">Day 3</a></li>
        <li><a href="#day-4">Day 4</a></li>
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                            <span>目录</span>
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  <ul>
    <li><a href="#2021-美赛">2021-美赛</a>
      <ul>
        <li><a href="#day-1">Day 1</a></li>
        <li><a href="#day-2">Day 2</a></li>
        <li><a href="#day-3">Day 3</a></li>
        <li><a href="#day-4">Day 4</a></li>
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            </div><div class="post-content"><a class="post-dummy-target" id="2021-美赛"></a><h2>2021-美赛</h2>
<ul>
<li>队友：统计17 hcl &amp; xy</li>
<li>选题：D Music Influence</li>
<li>结果：待更新</li>
<li>论文：<a href="https://www.jianguoyun.com/p/DfkJd_8Q29T5CBjPl-ED" target="_blank">2104092.pdf</a></li>
</ul>
<a class="post-dummy-target" id="day-1"></a><h3>Day 1</h3>
<p>第一天上午开始选题，本来一直做的C大数据题，后来发现今年的大数据正样本太少（只有14个正样本，2000多个负样本）小样本学习不太了解，总觉得不靠谱，然后改做D，第一天把第一问做完了，直接建立了影响力网络，然后通过普通的方法确定权重，整个1到4问的思路也出来了，第一问通过网络确定影响力，第二、三问通过音乐相似度确定影响力，第四问比较这两种方式确定的影响力是否同一。在第一天完成了第一问（使用了Pajek，安装在了AppData的Local下，一个网络科学的绘制、分析工具），第二问两位统计的同学使用了余弦相似度和皮尔森相似度模型，从我个人角度出发，我认为改进的余弦相似度会更好（未改进的对于数值过于不敏感，同一方向，数值不同，本质上是不相似的，但余弦相似度会给出相似的结论，通过减去指标平均值来改进），不过统计的同学还是用来皮尔森相似度。算出第一问的答案后，我开始等2，3问的结果。</p>
<a class="post-dummy-target" id="day-2"></a><h3>Day 2</h3>
<p>今天得到的2，3问结果和第一问差别太大&hellip;.内心其实比较崩溃，队伍气氛也有所下降，统计的两位同学把2，3问写完，我睁眼说瞎话把第四问写了，问题在于，第四问后半让分析出一个可以鉴定流派的指标，其实我们还是纠结于这个问法，如果它要做分类的话，其实我个人感觉直接用SVM支持向量机是可以做的，这个问法让我很奇怪，所以就草草了事，用平方差来度量样本的离散程度，并以此区分，问哪些特征具有感染力，我对指标进行排序，认为指标前10%的样本以及后10%的样本，具有特征，然后给所有艺术家标记特征，在影响力网络中，根据一条条边来看follower是否继承了influencer的该特征，然后统计，最后得出了几个特征具有这种“传染性”。</p>
<p>第二天晚上看到了PageRank算法，感觉第6问有了救星，这个算法是谷歌给网页重要程度进行排序的算法，同样可以用来找出一个网络中比较重要的节点，但是看见别人的思路里都没有提到这个，难道他们在藏拙？不过我认为这个算法应该是我们的亮点，计划在第三天完成这个算法。</p>
<a class="post-dummy-target" id="day-3"></a><h3>Day 3</h3>
<p>今天主要就是完成PageRank，然后写第6问？（可能是第四天早上弄完的PageRank，有点记不清了）</p>
<a class="post-dummy-target" id="day-4"></a><h3>Day 4</h3>
<p>印象中应该是今天早上完成的第六问，用PageRank做的，用Latex写了算法伪代码，画了文章框架图，找了音乐家的图片，加到图里（第六问爵士乐发展的那张图真的是我觉得很不错，可惜有点糊，用ppt加的人像，导不出SVG格式），然后写了敏感性分析，晚上就是在磨论文了，虽然感觉很多没有做（提到了缺失值处理但是没有弄，想做PCA主成分分析，也没有弄），但是也有相似度矩阵，还行。文章也有很多没有好好写，比如敏感性分析，模型也很简单，还有就是优缺点，问题假设，这些都有大漏洞，但是我英语确实匮乏，讲不出来，这一点需要花时间去克服。张来老师给改了摘要，xy的摘要确实思路清晰2333，我还以为会被骂。文章内容应该挺充实的，但问题也比较多，总体看来H应该问题不大（个人感觉比去年做的要好，去年完全就是相关度分析，相关度分析，弄得很糟糕），希望能进步拿个M吧（我们离O和F还差的很多，可能排版那块有F的水平了，但是内容&hellip;.），希望之后可以用Latex来写文章吧。</p>
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